Machine learning models are configured to identify category (also referred as classification) to which a problem or an object belongs to. The classification may be based on a training set of data containing observations whose classification is known. Determining classification of text may include converting the text into its vector representation and then providing the vector representation to a classifier. The classifier understands the vector representation of texts and henceforth learn the categories associated to each of the texts. One or more existing neural networks and models are configured to convert the texts to its corresponding vector representation. Output of the neural networks are vectors with float values which correspond to the vector representations of the texts. One of the neural networks include Long Short Term Memory (LSTM) units which are used in field of text analytics. The LSTM units have an ability to map sequence of texts (also referred as words or sentences of variable lengths) into corresponding vector representation.
The machine learning models may be trained for identifying the categories associated with text. The training may include identifying the hyperplanes between n-dimensional vector representations of texts from two different categories in training dataset. Existing method derives n-dimensional representation in an unsupervised manner (such as well-known tf-idf approach). Such training methods ignores comparison of each text with each of the other text based on category associated with each of the texts. Also, one or more existing systems do not disclose to include difference between category of the texts for training. The one or more existing systems may not be providing a desired performance of the classifiers. Also, generalization of the classifier is not accurate with the one or more existing systems.
The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.